Heterogeneity of ductal carcinoma in situ (DCIS) continues to be an important topic. Combining biomarker and
hematoxylin and eosin (HE) morphology information may provide more insights than either alone. We are working
towards a computer-based identification and description system for DCIS. As part of the system we are developing a
region of interest finder for further processing, such as identifying DCIS and other HE based measures.
The segmentation algorithm is designed to be tolerant of variability in staining and require no user interaction. To
achieve stain variation tolerance we use unsupervised learning and iteratively interrogate the image for information.
Using simple rules (e.g., “hematoxylin stains nuclei”) and iteratively assessing the resultant objects (small hematoxylin
stained objects are lymphocytes), the system builds up a knowledge base so that it is not dependent upon manual
annotations. The system starts with image resolution-based assumptions but these are replaced by knowledge gained.
The algorithm pipeline is designed to find the simplest items first (segment stains), then interesting subclasses and
objects (stroma, lymphocytes), and builds information until it is possible to segment blobs that are normal, DCIS, and
the range of benign glands. Once the blobs are found, features can be obtained and DCIS detected. In this work we
present the early segmentation results with stains where hematoxylin ranges from blue dominant to red dominant in RGB